Research Article
Multi-disciplinary Learning and Innovation for Professional Design of AI-Powered Services
@INPROCEEDINGS{10.1007/978-3-031-06675-7_2, author={Pontus W\aa{}rnest\ae{}l}, title={Multi-disciplinary Learning and Innovation for Professional Design of AI-Powered Services}, proceedings={Design, Learning, and Innovation. 6th EAI International Conference, DLI 2021, Virtual Event, December 10-11, 2021, Proceedings}, proceedings_a={DLI}, year={2022}, month={5}, keywords={AI Design Learning environments Innovation Digitalization}, doi={10.1007/978-3-031-06675-7_2} }- Pontus Wärnestål
Year: 2022
Multi-disciplinary Learning and Innovation for Professional Design of AI-Powered Services
DLI
Springer
DOI: 10.1007/978-3-031-06675-7_2
Abstract
Companies face several challenges when adopting Artificial Intelligence (AI) technologies in their service and product offerings. Adaptive behavior that changes over time, such as personalization, affects end-user experiences in sometimes unpredictable ways, making designing for AI-powered experiences difficult to prototype and evaluate. To fully make use of AI technologies, companies need new tools, methods, and knowledge that relate to their specific design context. This includes learning how to adapt design and development processes to fit AI-powered services, communication in cross-functional teams, and continuous competency development strategies. This paper reports on an innovation and learning program called AI.m that facilitates practical learning about how to use emerging AI technologies for human-centered design. The program has been executed for 15 companies and evaluated using interviews with researchers, design practitioners, and company representatives that have worked within the learning program. This study suggests and verifies a productive and efficient learning environment and process where companies, university research departments, and design agencies collaborate to produce AI-powered services and at the same time develop their competency in AI and human-centered design. The qualitative analysis provides a set of categories of learning implications organized as a framework of prompts to help organizations develop AI and design capabilities.
